In addition, the proposed method was slightly better than the standard joint training, i.e. SE+Conformer(L). To investigate the difference between each SE frontend, we also evaluated source separation performance using scale-invariant SNR (SI-SNR), scale-invariant signal-to-interference-ratio (SI-SIR), and scale-invariant SAR (SI-SAR) [1]}. Table REF  shows these scores of SE+Conformer(L) and the proposed method.
In SE+Conformer(L), SI-SNR has increased from the input SNR 7.5 dB, that means the SE-frontend reduces background noise well. In SNRi+Conformer(L), SI-SAR was 1.5 dB higher than that of SE+Conformer(L), whereas SI-SIR was 9.3 dB lower. That implies the proposed method limited the noise reduction performance of the SE frontend to avoid speech artifacts, and resulting in further improving the ASR performance.
